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Now showing items 11-17 of 17
Sparse Channel Estimation for Space-Time Block Coded OFDM-Based Underwater Acoustic Channels
(Institute of Electrical and Electronics Engineers Inc., 2018)
Communication over acoustic signals underwater results in multi-scale multi-lag channels due to multipath propagation. Hence a robust channel estimation technique has to be present at the receiver. In this paper assuming ...
Anomaly detection in walking trajectory [Yürüyüş yörüngesinde anormallik algılama]
(Institute of Electrical and Electronics Engineers Inc., 2018)
Analysis of the walking trajectory and the detection of anomalies in this trajectory provide important benefits in the fields of health and security. In this work two methods to detect anomalies in trajectories are compared. ...
Complex event post processing for traffic accidents
(IEEE, 2012)
In this paper we describe a framework for an expert system that tries to predict effects of an accident based on past data using supervised learning employing artificial neural networks. For this purpose sensory data events ...
Anomaly Detection in Walking Trajectory
(IEEE, 2018)
Analysis of the walking trajectory and the detection of anomalies in this trajectory, provide important benefits in the fields of health and security. In this work, two methods to detect anomalies in trajectories, are ...
Mathematical models for phase transitions in biogels
(World Scientific Publ Co Pte Ltd, 2019)
It has been shown that reversible and irreversible phase transitions of biogels can be represented by epidemic models. The irreversible chemical sol-gel transitions are modeled by the Susceptible-Exposed-Infected-Removed ...
A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data
(Mdpi, 2020)
Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease ...
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy
(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020)
Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data ...